code: def _pad_arrs_to_max_length(
      arrs, pad_val, dtype, batch_dim=0, round_to=None
  ):
      """Inner Implementation of the Pad batchify
  
      Parameters
      ----------
      arrs
          List of arrays
      pad_val
          The padding value
      dtype
          The type of the tensor
      batch_dim
          The dimension to insert the batch dimension.
          This controls how we should construct the mini-batch.
      round_to
          To round the size of the arrays to the closest multiply of round_to.
          This helps ensure the shape of the input tensor.
  
      Returns
      -------
      ret : th.Tensor
          The returned tensor
      """
      # First step is to convert the arrays to torch tensor
      if not isinstance(arrs[0], th.Tensor):
          arrs = [th.tensor(ele, dtype=dtype) for ele in arrs]
      dtype = arrs[0].dtype if dtype is None else dtype
      max_shape = list(arrs[0].shape)
      assert 0 <= batch_dim <= arrs[0].ndim
      for pad_axis in range(len(max_shape)):
          curr_lengths = [ele.shape[pad_axis] for ele in arrs]
          max_size = max(curr_lengths)
          if round_to is not None:
              max_size = round_to * math.ceil(max_size / round_to)
          max_shape[pad_axis] = max_size
      ret_shape = (
          tuple(max_shape[:batch_dim])
          + (len(arrs),)
          + tuple(max_shape[batch_dim:])
      )
  
      # Construct the full output
      ret = th.full(size=ret_shape, fill_value=pad_val, dtype=dtype)
  
      for i, arr in enumerate(arrs):
          slices = [slice(None) for _ in range(len(max_shape))]
          for j in range(len(max_shape)):
              if arr.shape[j] < max_shape[j]:
                  slices[j] = slice(0, arr.shape[j])
          slices.insert(batch_dim, i)
          ret[tuple(slices)] = arr
      return ret
  
imports: import math
  from typing import AnyStr
  from typing import Callable
  from typing import Dict
  from typing import List
  from typing import NamedTuple
  from typing import Tuple
  from typing import Union
  import numpy
  import torch
  from ..utils import to_torch_dtype
label_desc: Use image node_id 1 for calling a global function with example usage: _pad_arrs_to_max_length(arrs, pad_val, dtype, batch_dim, round_to) and returns: ret